It is known to use control charts in monitoring streams of data to identify if the mean or variability of the dataset fall outside predetermined control limits. Conventional control charts for the mean (for example, X-bar charts for control limits based on the mean) are effective for their intended purpose. In contrast, control charts for variability, which rely heavily on normality assumptions which are often violated in practice, are inaccurate and often provide erroneous indications of alterations in datastream variability (false positives and/or false negatives).
Conventional control charts for the range assume a normal distribution of data (i.e. set controls limits based on normality), and calculate parameters using a constant based on the distribution of the range. Thus, conventional control charts are symmetric, which is disadvantageous when the data obtained exhibit a skewed distribution. Disadvantageously, for equal subsample sizes, conventional (normal theory) control charts are the only control charts provided by many statistical computing packages. Accordingly, when the data distribution is not normal, conventional control charts perform poorly in assessing variability of a datastream collected and monitored in real time.
A need is therefore identified in the art for methods for real-time monitoring of variability of a datastream wherein the data are not bound by assumptions of normality. The method should provide accurate and rapid detection of such variability, and should be capable of accurately establishing new control limits for the range which reflect the true distribution of the data. The method of the present invention satisfies this need in the art, allowing monitoring of a desired data parameter or parameters with reduced risk of false positives/negatives. It will be appreciated from the following disclosure that the method allows real-time monitoring of the data parameter or parameters, and indeed further allows prediction of future alterations in the data parameter. The method achieves the desired result without requiring an inordinate number of computations, thereby improving computational efficiency.